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Optimizing Manufacturing through Process Mining

Optimizing Manufacturing through Process Mining

Optimizing Manufacturing through Process Mining

One of the main objectives of process optimization is to identify the current state of the customer’s production processes. Very often, provided process documentation is fragmented, incomplete, and outdated.

Problems with traditional process optimization

To create an accurate process model for our client, we analyze all available documentation and begin a process of refinement, interviewing, and observation through process mapping workshops. In traditional value stream mapping (VSM), consultants track sequential workflow events with a timer in hand to capture all value-adding activities. 

At Altezza Creative Solutions, we’ve encountered a couple of problems with the VSM approach:

  • VSM is time-consuming and requires collaboration with production floor workers, taking time away from routine tasks.
  • COVID limitations can prevent travel to remote production and observation of the process.
  • VSM provides only a static snapshot of process flows.
  • Data collection is restricted to a limited observation period.
  • VSM provides a high-level, end-to-end view, but more detail is needed to optimize the entire workflow.

Once the mapping is complete, we need an additional process data set to complete the model. Because of the functional fragmentation of organizations, this data is not always available. Moreover, each department may define key metrics differently. Enterprise Resource Planning (ERP) systems store a lot of process-related data, but you can’t easily find trends and patterns in information stored in hundreds of tables. Scattered records make it difficult to correlate process instances (e.g., a single production order) with fulfillment activities.

Using Process Mining for Digital Tracing

The problems of traditional VSM can be avoided by automating process modeling.

Production machinery can collect very detailed information from event logs about completed operations and transfer it back to the data warehouse. These records are time-stamped and correlated with process data in information systems such as enterprise resource planning systems, production management systems, and warehouse management systems. Suppose every action in a manufacturing process is captured in a reliable, complete event log and digitally recorded. In that case, it is possible to recreate an accurate process model from multiple historical event records.

In addition, all metrics associated with the process are measured from the event records. This provides an accurate snapshot of process performance, unlike metrics collected by traditional methods.

Process mining reduces the time it takes to create a process model and allows metrics to be calculated accurately and reliably. We can find process waste caused by defects, overprocessing, and delays faster with process discovery. 

Regardless of the method used to map the value stream and create the process model, the goal is to understand the real state of manufacturing operations. When the real state of operations becomes clear, the next step is to identify process inefficiencies.

Combining traditional VSM methods and process mining opens up new methods for process optimization. Analysts can identify bottlenecks preventing fully optimized processes when comparing process models dynamically generated from real data with a VSM model.

Altezza Creative Solutions has developed a methodology for process improvement that combines dynamic models created through process mining with existing static models:

  • Setting up a process analysis toolkit and extracting data. If available, it also includes analysis and evaluation of existing process documentation and process maps created by VSM methods.
  • Diagnostics of the implemented process and its consistency with the designed process. This phase also includes the identification of inefficiencies and non-value-adding activities.
  • Evaluation of potential solutions to optimize the process in the decision phase. For each proposed option, a projected return on investment is calculated.
  • Implementation – a combination of business process management and change management techniques for the transformation.

When evaluating potential process improvement initiatives, we prioritize those that can be scaled to an enterprise-wide scale and deliver the maximum return on investment after implementation.

Our expertise in technology-driven process improvement can help businesses increase their investments in innovative manufacturing initiatives and thrive in the “new normal.” 

As an experienced consulting partner in AI/ML, big data, and cloud-based manufacturing technologies, we help our clients adapt and succeed in a rapidly changing industry.

Let’s talk about how Altezza Creative Solutions can help you optimize your business processes using our profound expertise.